Overview

Brought to you by YData

Dataset statistics

Number of variables24
Number of observations7684
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory6.2 MiB
Average record size in memory840.6 B

Variable types

DateTime3
Numeric10
Text6
Categorical4
Boolean1

Alerts

customer_gender is highly overall correlated with customer_id and 2 other fieldsHigh correlation
customer_id is highly overall correlated with customer_gender and 2 other fieldsHigh correlation
email_domain is highly overall correlated with customer_gender and 2 other fieldsHigh correlation
gender_encoded is highly overall correlated with customer_gender and 2 other fieldsHigh correlation
high_value_item is highly overall correlated with item_priceHigh correlation
item_id is highly overall correlated with order_id and 1 other fieldsHigh correlation
item_price is highly overall correlated with high_value_item and 2 other fieldsHigh correlation
item_unit_total is highly overall correlated with item_price and 1 other fieldsHigh correlation
order_id is highly overall correlated with item_id and 1 other fieldsHigh correlation
order_total is highly overall correlated with item_price and 1 other fieldsHigh correlation
product_id is highly overall correlated with item_id and 1 other fieldsHigh correlation
customer_gender is highly imbalanced (63.7%) Imbalance
email_domain is highly imbalanced (60.3%) Imbalance
gender_encoded is highly imbalanced (63.7%) Imbalance
item_qty_order is highly skewed (γ1 = 47.15205892) Skewed
item_id has unique values Unique
order_total has 418 (5.4%) zeros Zeros
total_qty_ordered has 202 (2.6%) zeros Zeros
item_price has 265 (3.4%) zeros Zeros
item_unit_total has 265 (3.4%) zeros Zeros

Reproduction

Analysis started2025-06-07 10:31:00.151046
Analysis finished2025-06-07 10:31:36.291976
Duration36.14 seconds
Software versionydata-profiling vv4.16.1
Download configurationconfig.json

Variables

Distinct4754
Distinct (%)61.9%
Missing0
Missing (%)0.0%
Memory size60.2 KiB
Minimum2016-11-23 12:12:16+00:00
Maximum2021-08-29 08:54:01+00:00
Invalid dates0
Invalid dates (%)0.0%
2025-06-07T16:01:36.576462image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-07T16:01:36.963764image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

item_id
Real number (ℝ)

High correlation  Unique 

Distinct7684
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean102547.99
Minimum13
Maximum409358
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size60.2 KiB
2025-06-07T16:01:37.408557image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum13
5-th percentile636.15
Q14173.75
median10115.5
Q323268.25
95-th percentile404200.85
Maximum409358
Range409345
Interquartile range (IQR)19094.5

Descriptive statistics

Standard deviation166690.06
Coefficient of variation (CV)1.6254834
Kurtosis-0.57652369
Mean102547.99
Median Absolute Deviation (MAD)7454.5
Skewness1.1893763
Sum7.8797878 × 108
Variance2.7785576 × 1010
MonotonicityNot monotonic
2025-06-07T16:01:37.763569image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
408776 1
 
< 0.1%
13 1
 
< 0.1%
43 1
 
< 0.1%
53 1
 
< 0.1%
404844 1
 
< 0.1%
404421 1
 
< 0.1%
404252 1
 
< 0.1%
404237 1
 
< 0.1%
404191 1
 
< 0.1%
403730 1
 
< 0.1%
Other values (7674) 7674
99.9%
ValueCountFrequency (%)
13 1
< 0.1%
14 1
< 0.1%
15 1
< 0.1%
41 1
< 0.1%
43 1
< 0.1%
44 1
< 0.1%
45 1
< 0.1%
47 1
< 0.1%
48 1
< 0.1%
53 1
< 0.1%
ValueCountFrequency (%)
409358 1
< 0.1%
409357 1
< 0.1%
409352 1
< 0.1%
409351 1
< 0.1%
409213 1
< 0.1%
409212 1
< 0.1%
409204 1
< 0.1%
409164 1
< 0.1%
409163 1
< 0.1%
409114 1
< 0.1%

order_id
Real number (ℝ)

High correlation 

Distinct4787
Distinct (%)62.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean56561.859
Minimum0
Maximum227752
Zeros5
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size60.2 KiB
2025-06-07T16:01:38.187853image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile341.75
Q12538.75
median6498.5
Q316060
95-th percentile223736.85
Maximum227752
Range227752
Interquartile range (IQR)13521.25

Descriptive statistics

Standard deviation90899.775
Coefficient of variation (CV)1.6070861
Kurtosis-0.56513151
Mean56561.859
Median Absolute Deviation (MAD)4945
Skewness1.1908462
Sum4.3462132 × 108
Variance8.2627691 × 109
MonotonicityNot monotonic
2025-06-07T16:01:38.625520image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
224889 89
 
1.2%
1406 15
 
0.2%
9829 15
 
0.2%
16035 11
 
0.1%
208011 11
 
0.1%
224888 10
 
0.1%
208007 10
 
0.1%
1057 9
 
0.1%
10153 9
 
0.1%
14922 9
 
0.1%
Other values (4777) 7496
97.6%
ValueCountFrequency (%)
0 5
0.1%
9 2
 
< 0.1%
10 1
 
< 0.1%
23 1
 
< 0.1%
24 2
 
< 0.1%
25 3
< 0.1%
28 1
 
< 0.1%
31 5
0.1%
32 1
 
< 0.1%
33 1
 
< 0.1%
ValueCountFrequency (%)
227752 2
< 0.1%
227747 2
< 0.1%
227635 2
< 0.1%
227627 1
< 0.1%
227593 2
< 0.1%
227568 1
< 0.1%
227550 2
< 0.1%
227543 1
< 0.1%
227486 1
< 0.1%
227401 1
< 0.1%
Distinct4787
Distinct (%)62.3%
Missing0
Missing (%)0.0%
Memory size501.8 KiB
2025-06-07T16:01:39.378663image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length16
Median length15
Mean length9.8551536
Min length3

Characters and Unicode

Total characters75727
Distinct characters21
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique3038 ?
Unique (%)39.5%

Sample

1st rowA1123000010463
2nd rowA1128000027180
3rd rowA1128000032136
4th rowA1128000037298
5th rowA1206000072983
ValueCountFrequency (%)
bob 147
 
1.9%
b1bos011121-2 89
 
1.1%
bou04302 15
 
0.2%
bou011091793 15
 
0.2%
bou11770869 11
 
0.1%
bou05371738 11
 
0.1%
bos116908 10
 
0.1%
b1bos011121-1 10
 
0.1%
bou05390151 9
 
0.1%
bou03315 9
 
0.1%
Other values (4778) 7505
95.8%
2025-06-07T16:01:40.361151image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 12185
16.1%
0 9867
13.0%
B 8344
11.0%
O 7420
9.8%
2 6037
8.0%
U 5642
7.5%
3 4520
 
6.0%
5 4075
 
5.4%
9 4036
 
5.3%
7 3640
 
4.8%
Other values (11) 9961
13.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 75727
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 12185
16.1%
0 9867
13.0%
B 8344
11.0%
O 7420
9.8%
2 6037
8.0%
U 5642
7.5%
3 4520
 
6.0%
5 4075
 
5.4%
9 4036
 
5.3%
7 3640
 
4.8%
Other values (11) 9961
13.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 75727
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 12185
16.1%
0 9867
13.0%
B 8344
11.0%
O 7420
9.8%
2 6037
8.0%
U 5642
7.5%
3 4520
 
6.0%
5 4075
 
5.4%
9 4036
 
5.3%
7 3640
 
4.8%
Other values (11) 9961
13.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 75727
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 12185
16.1%
0 9867
13.0%
B 8344
11.0%
O 7420
9.8%
2 6037
8.0%
U 5642
7.5%
3 4520
 
6.0%
5 4075
 
5.4%
9 4036
 
5.3%
7 3640
 
4.8%
Other values (11) 9961
13.2%

order_total
Real number (ℝ)

High correlation  Zeros 

Distinct2198
Distinct (%)28.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6288.4659
Minimum0
Maximum473800
Zeros418
Zeros (%)5.4%
Negative0
Negative (%)0.0%
Memory size60.2 KiB
2025-06-07T16:01:40.685774image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q1583
median2425
Q35845.35
95-th percentile21510
Maximum473800
Range473800
Interquartile range (IQR)5262.35

Descriptive statistics

Standard deviation20415.032
Coefficient of variation (CV)3.2464249
Kurtosis288.85813
Mean6288.4659
Median Absolute Deviation (MAD)2103.5
Skewness14.741033
Sum48320572
Variance4.1677354 × 108
MonotonicityNot monotonic
2025-06-07T16:01:41.140956image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 418
 
5.4%
54119 89
 
1.2%
2425 80
 
1.0%
2450 56
 
0.7%
22 36
 
0.5%
16 32
 
0.4%
200 30
 
0.4%
3500 28
 
0.4%
275 26
 
0.3%
925 24
 
0.3%
Other values (2188) 6865
89.3%
ValueCountFrequency (%)
0 418
5.4%
6.75 1
 
< 0.1%
11 5
 
0.1%
12.38 1
 
< 0.1%
14.5 1
 
< 0.1%
15 2
 
< 0.1%
15.75 1
 
< 0.1%
16 32
 
0.4%
17 13
 
0.2%
19 1
 
< 0.1%
ValueCountFrequency (%)
473800 4
0.1%
423100 6
0.1%
196100 5
0.1%
193775 2
 
< 0.1%
159870 4
0.1%
154000 3
< 0.1%
137615 5
0.1%
132450 2
 
< 0.1%
126714 3
< 0.1%
122000 3
< 0.1%

total_qty_ordered
Real number (ℝ)

Zeros 

Distinct20
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.3515096
Minimum0
Maximum100
Zeros202
Zeros (%)2.6%
Negative0
Negative (%)0.0%
Memory size60.2 KiB
2025-06-07T16:01:41.429085image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q11
median2
Q33
95-th percentile5
Maximum100
Range100
Interquartile range (IQR)2

Descriptive statistics

Standard deviation2.5301918
Coefficient of variation (CV)1.0759862
Kurtosis369.58396
Mean2.3515096
Median Absolute Deviation (MAD)1
Skewness12.649151
Sum18069
Variance6.4018707
MonotonicityNot monotonic
2025-06-07T16:01:41.684020image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=20)
ValueCountFrequency (%)
1 3073
40.0%
2 1939
25.2%
3 1087
 
14.1%
4 750
 
9.8%
5 269
 
3.5%
0 202
 
2.6%
6 96
 
1.2%
7 61
 
0.8%
8 47
 
0.6%
11 46
 
0.6%
Other values (10) 114
 
1.5%
ValueCountFrequency (%)
0 202
 
2.6%
1 3073
40.0%
2 1939
25.2%
3 1087
 
14.1%
4 750
 
9.8%
5 269
 
3.5%
6 96
 
1.2%
7 61
 
0.8%
8 47
 
0.6%
9 26
 
0.3%
ValueCountFrequency (%)
100 1
 
< 0.1%
60 2
 
< 0.1%
30 3
 
< 0.1%
21 6
 
0.1%
20 2
 
< 0.1%
17 15
 
0.2%
14 6
 
0.1%
12 24
0.3%
11 46
0.6%
10 29
0.4%

customer_id
Real number (ℝ)

High correlation 

Distinct54
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean30.581598
Minimum4
Maximum149
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size60.2 KiB
2025-06-07T16:01:42.050625image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum4
5-th percentile24
Q124
median24
Q341
95-th percentile67
Maximum149
Range145
Interquartile range (IQR)17

Descriptive statistics

Standard deviation16.332019
Coefficient of variation (CV)0.53404727
Kurtosis16.361913
Mean30.581598
Median Absolute Deviation (MAD)0
Skewness3.505884
Sum234989
Variance266.73485
MonotonicityNot monotonic
2025-06-07T16:01:42.389499image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
24 5289
68.8%
41 1354
 
17.6%
16 302
 
3.9%
67 168
 
2.2%
98 84
 
1.1%
49 74
 
1.0%
48 70
 
0.9%
79 47
 
0.6%
46 31
 
0.4%
20 20
 
0.3%
Other values (44) 245
 
3.2%
ValueCountFrequency (%)
4 16
 
0.2%
5 5
 
0.1%
6 11
 
0.1%
7 3
 
< 0.1%
10 7
 
0.1%
13 3
 
< 0.1%
14 7
 
0.1%
16 302
3.9%
19 1
 
< 0.1%
20 20
 
0.3%
ValueCountFrequency (%)
149 7
0.1%
148 17
0.2%
140 2
 
< 0.1%
139 6
 
0.1%
137 1
 
< 0.1%
134 12
0.2%
122 1
 
< 0.1%
111 1
 
< 0.1%
109 1
 
< 0.1%
106 12
0.2%
Distinct54
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Memory size516.9 KiB
2025-06-07T16:01:42.977791image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length18
Median length12
Mean length11.864133
Min length9

Characters and Unicode

Total characters91164
Distinct characters45
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique10 ?
Unique (%)0.1%

Sample

1st rowShantae Guseo
2nd rowLinn Finco
3rd rowJoellen Best
4th rowLinn Finco
5th rowChung Bolger
ValueCountFrequency (%)
chung 5289
34.4%
bolger 5289
34.4%
rex 1354
 
8.8%
ebonie 1354
 
8.8%
cheryl 302
 
2.0%
mcginnis 302
 
2.0%
evonne 168
 
1.1%
elenora 168
 
1.1%
sherryl 84
 
0.5%
socolow 84
 
0.5%
Other values (76) 974
 
6.3%
2025-06-07T16:01:43.799674image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
g 10892
11.9%
e 9496
10.4%
n 8407
9.2%
7684
8.4%
o 7543
8.3%
r 6272
 
6.9%
l 6214
 
6.8%
h 5718
 
6.3%
C 5658
 
6.2%
u 5337
 
5.9%
Other values (35) 17943
19.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 91164
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
g 10892
11.9%
e 9496
10.4%
n 8407
9.2%
7684
8.4%
o 7543
8.3%
r 6272
 
6.9%
l 6214
 
6.8%
h 5718
 
6.3%
C 5658
 
6.2%
u 5337
 
5.9%
Other values (35) 17943
19.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 91164
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
g 10892
11.9%
e 9496
10.4%
n 8407
9.2%
7684
8.4%
o 7543
8.3%
r 6272
 
6.9%
l 6214
 
6.8%
h 5718
 
6.3%
C 5658
 
6.2%
u 5337
 
5.9%
Other values (35) 17943
19.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 91164
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
g 10892
11.9%
e 9496
10.4%
n 8407
9.2%
7684
8.4%
o 7543
8.3%
r 6272
 
6.9%
l 6214
 
6.8%
h 5718
 
6.3%
C 5658
 
6.2%
u 5337
 
5.9%
Other values (35) 17943
19.7%

customer_gender
Categorical

High correlation  Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size471.8 KiB
Female
7152 
Male
 
532

Length

Max length6
Median length6
Mean length5.8615305
Min length4

Characters and Unicode

Total characters45040
Distinct characters6
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowMale
2nd rowFemale
3rd rowMale
4th rowFemale
5th rowFemale

Common Values

ValueCountFrequency (%)
Female 7152
93.1%
Male 532
 
6.9%

Length

2025-06-07T16:01:44.165942image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-06-07T16:01:44.466191image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
female 7152
93.1%
male 532
 
6.9%

Most occurring characters

ValueCountFrequency (%)
e 14836
32.9%
a 7684
17.1%
l 7684
17.1%
F 7152
15.9%
m 7152
15.9%
M 532
 
1.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 45040
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 14836
32.9%
a 7684
17.1%
l 7684
17.1%
F 7152
15.9%
m 7152
15.9%
M 532
 
1.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 45040
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 14836
32.9%
a 7684
17.1%
l 7684
17.1%
F 7152
15.9%
m 7152
15.9%
M 532
 
1.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 45040
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 14836
32.9%
a 7684
17.1%
l 7684
17.1%
F 7152
15.9%
m 7152
15.9%
M 532
 
1.2%
Distinct54
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Memory size553.3 KiB
2025-06-07T16:01:44.777244image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length21
Median length17
Mean length16.712129
Min length12

Characters and Unicode

Total characters128416
Distinct characters27
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique10 ?
Unique (%)0.1%

Sample

1st rowdebest@verizon.net
2nd rowshang@live.com
3rd rowlbecchi@verizon.net
4th rowshang@live.com
5th rowhyper@outlook.com
ValueCountFrequency (%)
hyper@outlook.com 5289
68.8%
lahvak@yahoo.com 1354
 
17.6%
mugwump@att.net 302
 
3.9%
dieman@verizon.net 168
 
2.2%
hling@hotmail.com 84
 
1.1%
jgwang@att.net 74
 
1.0%
inico@live.com 70
 
0.9%
gmcgath@att.net 47
 
0.6%
kspiteri@me.com 31
 
0.4%
shang@live.com 20
 
0.3%
Other values (44) 245
 
3.2%
2025-06-07T16:01:45.598155image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
o 26393
20.6%
h 8406
 
6.5%
m 8086
 
6.3%
. 7684
 
6.0%
@ 7684
 
6.0%
c 7308
 
5.7%
t 7155
 
5.6%
l 7097
 
5.5%
y 6762
 
5.3%
k 6736
 
5.2%
Other values (17) 35105
27.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 128416
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
o 26393
20.6%
h 8406
 
6.5%
m 8086
 
6.3%
. 7684
 
6.0%
@ 7684
 
6.0%
c 7308
 
5.7%
t 7155
 
5.6%
l 7097
 
5.5%
y 6762
 
5.3%
k 6736
 
5.2%
Other values (17) 35105
27.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 128416
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
o 26393
20.6%
h 8406
 
6.5%
m 8086
 
6.3%
. 7684
 
6.0%
@ 7684
 
6.0%
c 7308
 
5.7%
t 7155
 
5.6%
l 7097
 
5.5%
y 6762
 
5.3%
k 6736
 
5.2%
Other values (17) 35105
27.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 128416
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
o 26393
20.6%
h 8406
 
6.5%
m 8086
 
6.3%
. 7684
 
6.0%
@ 7684
 
6.0%
c 7308
 
5.7%
t 7155
 
5.6%
l 7097
 
5.5%
y 6762
 
5.3%
k 6736
 
5.2%
Other values (17) 35105
27.3%

product_id
Real number (ℝ)

High correlation 

Distinct4296
Distinct (%)55.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean12706491
Minimum53067
Maximum1.6135317 × 108
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size60.2 KiB
2025-06-07T16:01:45.936116image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum53067
5-th percentile55661.55
Q167710.75
median125040
Q3215058
95-th percentile1.6105976 × 108
Maximum1.6135317 × 108
Range1.613001 × 108
Interquartile range (IQR)147347.25

Descriptive statistics

Standard deviation43171656
Coefficient of variation (CV)3.3976065
Kurtosis7.8973055
Mean12706491
Median Absolute Deviation (MAD)62786
Skewness3.1456502
Sum9.7636675 × 1010
Variance1.8637919 × 1015
MonotonicityNot monotonic
2025-06-07T16:01:46.352161image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
86775 217
 
2.8%
125040 130
 
1.7%
84037 103
 
1.3%
60971 74
 
1.0%
63334 50
 
0.7%
277139 34
 
0.4%
105533 33
 
0.4%
105666 31
 
0.4%
60980 26
 
0.3%
62449 26
 
0.3%
Other values (4286) 6960
90.6%
ValueCountFrequency (%)
53067 3
< 0.1%
53071 3
< 0.1%
53072 1
 
< 0.1%
53073 1
 
< 0.1%
53076 2
 
< 0.1%
53081 5
0.1%
53082 1
 
< 0.1%
53089 6
0.1%
53092 1
 
< 0.1%
53093 4
0.1%
ValueCountFrequency (%)
161353169 1
 
< 0.1%
161353047 6
 
0.1%
161352739 1
 
< 0.1%
161351820 1
 
< 0.1%
161351766 2
 
< 0.1%
161351699 2
 
< 0.1%
161351683 7
0.1%
161351579 16
0.2%
161351534 1
 
< 0.1%
161351414 1
 
< 0.1%
Distinct4296
Distinct (%)55.9%
Missing0
Missing (%)0.0%
Memory size495.4 KiB
2025-06-07T16:01:46.914039image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length18
Median length9
Mean length9.0078084
Min length8

Characters and Unicode

Total characters69216
Distinct characters27
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique3239 ?
Unique (%)42.2%

Sample

1st row210768756
2nd row210759724
3rd row210810839
4th row210759761
5th row210763929
ValueCountFrequency (%)
210173084 217
 
2.8%
211532988 130
 
1.7%
211230946 103
 
1.3%
210942128 74
 
1.0%
209630413 50
 
0.7%
202545536 34
 
0.4%
204770019 33
 
0.4%
211104825 31
 
0.4%
210942137 26
 
0.3%
212232167 26
 
0.3%
Other values (4286) 6960
90.6%
2025-06-07T16:01:47.692137image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
2 14732
21.3%
1 12994
18.8%
0 7932
11.5%
7 5189
 
7.5%
9 5014
 
7.2%
8 4871
 
7.0%
4 4720
 
6.8%
3 4484
 
6.5%
6 4450
 
6.4%
5 4441
 
6.4%
Other values (17) 389
 
0.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 69216
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
2 14732
21.3%
1 12994
18.8%
0 7932
11.5%
7 5189
 
7.5%
9 5014
 
7.2%
8 4871
 
7.0%
4 4720
 
6.8%
3 4484
 
6.5%
6 4450
 
6.4%
5 4441
 
6.4%
Other values (17) 389
 
0.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 69216
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
2 14732
21.3%
1 12994
18.8%
0 7932
11.5%
7 5189
 
7.5%
9 5014
 
7.2%
8 4871
 
7.0%
4 4720
 
6.8%
3 4484
 
6.5%
6 4450
 
6.4%
5 4441
 
6.4%
Other values (17) 389
 
0.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 69216
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
2 14732
21.3%
1 12994
18.8%
0 7932
11.5%
7 5189
 
7.5%
9 5014
 
7.2%
8 4871
 
7.0%
4 4720
 
6.8%
3 4484
 
6.5%
6 4450
 
6.4%
5 4441
 
6.4%
Other values (17) 389
 
0.6%
Distinct3480
Distinct (%)45.3%
Missing0
Missing (%)0.0%
Memory size719.4 KiB
2025-06-07T16:01:48.486935image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length107
Median length79
Mean length32.255726
Min length7

Characters and Unicode

Total characters247853
Distinct characters119
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2260 ?
Unique (%)29.4%

Sample

1st row210768942
2nd rowBlack Long Velvet Jacket
3rd rowKEELY 100 SATIN BOW 100MM MULE:Light/Pastel Pink :39.5 - 210809529
4th rowOff-White Bateau Neck Long Tank Top
5th rowPainted Blue Kimberly Plaid Shirt
ValueCountFrequency (%)
black 1150
 
3.1%
896
 
2.4%
bag 487
 
1.3%
leather 483
 
1.3%
gold 455
 
1.2%
white 453
 
1.2%
red 424
 
1.2%
dress 416
 
1.1%
set 298
 
0.8%
pumps 270
 
0.7%
Other values (3965) 31334
85.5%
2025-06-07T16:01:49.896362image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
28984
 
11.7%
e 22317
 
9.0%
a 15733
 
6.3%
l 13213
 
5.3%
r 12674
 
5.1%
i 11845
 
4.8%
t 10521
 
4.2%
o 10354
 
4.2%
n 9019
 
3.6%
s 7786
 
3.1%
Other values (109) 105407
42.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 247853
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
28984
 
11.7%
e 22317
 
9.0%
a 15733
 
6.3%
l 13213
 
5.3%
r 12674
 
5.1%
i 11845
 
4.8%
t 10521
 
4.2%
o 10354
 
4.2%
n 9019
 
3.6%
s 7786
 
3.1%
Other values (109) 105407
42.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 247853
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
28984
 
11.7%
e 22317
 
9.0%
a 15733
 
6.3%
l 13213
 
5.3%
r 12674
 
5.1%
i 11845
 
4.8%
t 10521
 
4.2%
o 10354
 
4.2%
n 9019
 
3.6%
s 7786
 
3.1%
Other values (109) 105407
42.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 247853
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
28984
 
11.7%
e 22317
 
9.0%
a 15733
 
6.3%
l 13213
 
5.3%
r 12674
 
5.1%
i 11845
 
4.8%
t 10521
 
4.2%
o 10354
 
4.2%
n 9019
 
3.6%
s 7786
 
3.1%
Other values (109) 105407
42.5%

item_price
Real number (ℝ)

High correlation  Zeros 

Distinct1419
Distinct (%)18.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2220.2441
Minimum0
Maximum67000
Zeros265
Zeros (%)3.4%
Negative0
Negative (%)0.0%
Memory size60.2 KiB
2025-06-07T16:01:50.231026image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile16
Q1215.24
median1047.62
Q32571.43
95-th percentile7550
Maximum67000
Range67000
Interquartile range (IQR)2356.19

Descriptive statistics

Standard deviation4494.1955
Coefficient of variation (CV)2.0241898
Kurtosis61.116443
Mean2220.2441
Median Absolute Deviation (MAD)902.5
Skewness6.7723654
Sum17060356
Variance20197793
MonotonicityNot monotonic
2025-06-07T16:01:50.642125image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 265
 
3.4%
16 209
 
2.7%
925 141
 
1.8%
1500 119
 
1.5%
175 93
 
1.2%
3000 72
 
0.9%
200 65
 
0.8%
2000 49
 
0.6%
1850 49
 
0.6%
1450 46
 
0.6%
Other values (1409) 6576
85.6%
ValueCountFrequency (%)
0 265
3.4%
5.1 1
 
< 0.1%
7.5 1
 
< 0.1%
9.29 1
 
< 0.1%
9.5 1
 
< 0.1%
9.75 1
 
< 0.1%
10.75 1
 
< 0.1%
12.25 1
 
< 0.1%
15.25 1
 
< 0.1%
16 209
2.7%
ValueCountFrequency (%)
67000 3
< 0.1%
57700 1
 
< 0.1%
48900 2
 
< 0.1%
48190.48 1
 
< 0.1%
47800 1
 
< 0.1%
45600 1
 
< 0.1%
45476.19 1
 
< 0.1%
45000 4
0.1%
44761.9 5
0.1%
43400 4
0.1%

item_qty_order
Real number (ℝ)

Skewed 

Distinct13
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.1353462
Minimum1
Maximum100
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size60.2 KiB
2025-06-07T16:01:50.934706image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median1
Q31
95-th percentile2
Maximum100
Range99
Interquartile range (IQR)0

Descriptive statistics

Standard deviation1.5634958
Coefficient of variation (CV)1.3771093
Kurtosis2604.7246
Mean1.1353462
Median Absolute Deviation (MAD)0
Skewness47.152059
Sum8724
Variance2.4445191
MonotonicityNot monotonic
2025-06-07T16:01:51.214515image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=13)
ValueCountFrequency (%)
1 7156
93.1%
2 374
 
4.9%
3 84
 
1.1%
4 37
 
0.5%
5 12
 
0.2%
6 6
 
0.1%
7 5
 
0.1%
8 4
 
0.1%
60 2
 
< 0.1%
100 1
 
< 0.1%
Other values (3) 3
 
< 0.1%
ValueCountFrequency (%)
1 7156
93.1%
2 374
 
4.9%
3 84
 
1.1%
4 37
 
0.5%
5 12
 
0.2%
6 6
 
0.1%
7 5
 
0.1%
8 4
 
0.1%
10 1
 
< 0.1%
12 1
 
< 0.1%
ValueCountFrequency (%)
100 1
 
< 0.1%
60 2
 
< 0.1%
15 1
 
< 0.1%
12 1
 
< 0.1%
10 1
 
< 0.1%
8 4
 
0.1%
7 5
 
0.1%
6 6
 
0.1%
5 12
 
0.2%
4 37
0.5%

item_unit_total
Real number (ℝ)

High correlation  Zeros 

Distinct1558
Distinct (%)20.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2414.6489
Minimum0
Maximum214285.7
Zeros265
Zeros (%)3.4%
Negative0
Negative (%)0.0%
Memory size60.2 KiB
2025-06-07T16:01:51.512400image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile16
Q1240
median1100
Q32666.67
95-th percentile8095.24
Maximum214285.7
Range214285.7
Interquartile range (IQR)2426.67

Descriptive statistics

Standard deviation6065.8839
Coefficient of variation (CV)2.5121183
Kurtosis299.88535
Mean2414.6489
Median Absolute Deviation (MAD)948
Skewness13.282973
Sum18554163
Variance36794947
MonotonicityNot monotonic
2025-06-07T16:01:51.944216image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 265
 
3.4%
16 183
 
2.4%
925 123
 
1.6%
1500 103
 
1.3%
3000 84
 
1.1%
175 74
 
1.0%
1850 58
 
0.8%
2000 50
 
0.7%
1000 49
 
0.6%
200 44
 
0.6%
Other values (1548) 6651
86.6%
ValueCountFrequency (%)
0 265
3.4%
5.1 1
 
< 0.1%
7.5 1
 
< 0.1%
9.29 1
 
< 0.1%
9.5 1
 
< 0.1%
9.75 1
 
< 0.1%
10.75 1
 
< 0.1%
12.25 1
 
< 0.1%
15.25 1
 
< 0.1%
16 183
2.4%
ValueCountFrequency (%)
214285.7 1
 
< 0.1%
130200 1
 
< 0.1%
128571.42 1
 
< 0.1%
114750 1
 
< 0.1%
89523.8 2
< 0.1%
79047.62 2
< 0.1%
79000 1
 
< 0.1%
68380.96 2
< 0.1%
67043.49 1
 
< 0.1%
67000 3
< 0.1%
Distinct811
Distinct (%)10.6%
Missing0
Missing (%)0.0%
Memory size300.3 KiB
Minimum2016-11-23 00:00:00
Maximum2021-08-29 00:00:00
Invalid dates0
Invalid dates (%)0.0%
2025-06-07T16:01:52.528008image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-07T16:01:52.997847image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
Distinct58
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Memory size60.2 KiB
Minimum2016-11-01 00:00:00
Maximum2021-08-01 00:00:00
Invalid dates0
Invalid dates (%)0.0%
2025-06-07T16:01:53.486248image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-07T16:01:53.861801image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
Distinct7
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size481.4 KiB
Tuesday
1799 
Monday
1437 
Thursday
1379 
Wednesday
1270 
Sunday
1206 
Other values (2)
593 

Length

Max length9
Median length8
Mean length7.1326132
Min length6

Characters and Unicode

Total characters54807
Distinct characters17
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowWednesday
2nd rowMonday
3rd rowMonday
4th rowMonday
5th rowTuesday

Common Values

ValueCountFrequency (%)
Tuesday 1799
23.4%
Monday 1437
18.7%
Thursday 1379
17.9%
Wednesday 1270
16.5%
Sunday 1206
15.7%
Friday 425
 
5.5%
Saturday 168
 
2.2%

Length

2025-06-07T16:01:54.284200image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-06-07T16:01:54.606481image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
tuesday 1799
23.4%
monday 1437
18.7%
thursday 1379
17.9%
wednesday 1270
16.5%
sunday 1206
15.7%
friday 425
 
5.5%
saturday 168
 
2.2%

Most occurring characters

ValueCountFrequency (%)
d 8954
16.3%
a 7852
14.3%
y 7684
14.0%
u 4552
8.3%
s 4448
8.1%
e 4339
7.9%
n 3913
7.1%
T 3178
 
5.8%
r 1972
 
3.6%
o 1437
 
2.6%
Other values (7) 6478
11.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 54807
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
d 8954
16.3%
a 7852
14.3%
y 7684
14.0%
u 4552
8.3%
s 4448
8.1%
e 4339
7.9%
n 3913
7.1%
T 3178
 
5.8%
r 1972
 
3.6%
o 1437
 
2.6%
Other values (7) 6478
11.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 54807
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
d 8954
16.3%
a 7852
14.3%
y 7684
14.0%
u 4552
8.3%
s 4448
8.1%
e 4339
7.9%
n 3913
7.1%
T 3178
 
5.8%
r 1972
 
3.6%
o 1437
 
2.6%
Other values (7) 6478
11.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 54807
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
d 8954
16.3%
a 7852
14.3%
y 7684
14.0%
u 4552
8.3%
s 4448
8.1%
e 4339
7.9%
n 3913
7.1%
T 3178
 
5.8%
r 1972
 
3.6%
o 1437
 
2.6%
Other values (7) 6478
11.8%

order_hour
Real number (ℝ)

Distinct23
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean10.312858
Minimum0
Maximum23
Zeros5
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size30.1 KiB
2025-06-07T16:01:54.894643image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile6
Q18
median10
Q312
95-th percentile16
Maximum23
Range23
Interquartile range (IQR)4

Descriptive statistics

Standard deviation3.5092687
Coefficient of variation (CV)0.34028091
Kurtosis1.6268977
Mean10.312858
Median Absolute Deviation (MAD)2
Skewness0.93058078
Sum79244
Variance12.314967
MonotonicityNot monotonic
2025-06-07T16:01:55.416400image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=23)
ValueCountFrequency (%)
7 913
11.9%
8 893
11.6%
11 855
11.1%
13 775
10.1%
10 771
10.0%
12 768
10.0%
9 699
9.1%
6 567
7.4%
14 500
6.5%
5 263
 
3.4%
Other values (13) 680
8.8%
ValueCountFrequency (%)
0 5
 
0.1%
1 4
 
0.1%
3 17
 
0.2%
4 39
 
0.5%
5 263
 
3.4%
6 567
7.4%
7 913
11.9%
8 893
11.6%
9 699
9.1%
10 771
10.0%
ValueCountFrequency (%)
23 46
 
0.6%
22 114
 
1.5%
21 30
 
0.4%
20 40
 
0.5%
19 12
 
0.2%
18 15
 
0.2%
17 74
 
1.0%
16 72
 
0.9%
15 212
2.8%
14 500
6.5%

high_value_item
Boolean

High correlation 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size7.6 KiB
False
6163 
True
1521 
ValueCountFrequency (%)
False 6163
80.2%
True 1521
 
19.8%
2025-06-07T16:01:55.596454image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

email_domain
Categorical

High correlation  Imbalance 

Distinct14
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size505.4 KiB
outlook.com
5307 
yahoo.com
1410 
att.net
 
427
verizon.net
 
179
hotmail.com
 
116
Other values (9)
 
245

Length

Max length13
Median length11
Mean length10.338365
Min length6

Characters and Unicode

Total characters79440
Distinct characters22
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowverizon.net
2nd rowlive.com
3rd rowverizon.net
4th rowlive.com
5th rowoutlook.com

Common Values

ValueCountFrequency (%)
outlook.com 5307
69.1%
yahoo.com 1410
 
18.3%
att.net 427
 
5.6%
verizon.net 179
 
2.3%
hotmail.com 116
 
1.5%
live.com 94
 
1.2%
me.com 41
 
0.5%
comcast.net 28
 
0.4%
icloud.com 22
 
0.3%
yahoo.ca 19
 
0.2%
Other values (4) 41
 
0.5%

Length

2025-06-07T16:01:55.844400image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
outlook.com 5307
69.1%
yahoo.com 1410
 
18.3%
att.net 427
 
5.6%
verizon.net 179
 
2.3%
hotmail.com 116
 
1.5%
live.com 94
 
1.2%
me.com 41
 
0.5%
comcast.net 28
 
0.4%
icloud.com 22
 
0.3%
yahoo.ca 19
 
0.2%
Other values (4) 41
 
0.5%

Most occurring characters

ValueCountFrequency (%)
o 26179
33.0%
. 7684
 
9.7%
m 7193
 
9.1%
c 7116
 
9.0%
t 6986
 
8.8%
l 5585
 
7.0%
u 5329
 
6.7%
k 5307
 
6.7%
a 2042
 
2.6%
h 1545
 
1.9%
Other values (12) 4474
 
5.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 79440
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
o 26179
33.0%
. 7684
 
9.7%
m 7193
 
9.1%
c 7116
 
9.0%
t 6986
 
8.8%
l 5585
 
7.0%
u 5329
 
6.7%
k 5307
 
6.7%
a 2042
 
2.6%
h 1545
 
1.9%
Other values (12) 4474
 
5.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 79440
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
o 26179
33.0%
. 7684
 
9.7%
m 7193
 
9.1%
c 7116
 
9.0%
t 6986
 
8.8%
l 5585
 
7.0%
u 5329
 
6.7%
k 5307
 
6.7%
a 2042
 
2.6%
h 1545
 
1.9%
Other values (12) 4474
 
5.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 79440
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
o 26179
33.0%
. 7684
 
9.7%
m 7193
 
9.1%
c 7116
 
9.0%
t 6986
 
8.8%
l 5585
 
7.0%
u 5329
 
6.7%
k 5307
 
6.7%
a 2042
 
2.6%
h 1545
 
1.9%
Other values (12) 4474
 
5.6%

gender_encoded
Categorical

High correlation  Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size435.4 KiB
0
7152 
1
 
532

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters7684
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row0
3rd row1
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 7152
93.1%
1 532
 
6.9%

Length

2025-06-07T16:01:56.088191image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-06-07T16:01:56.212421image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0 7152
93.1%
1 532
 
6.9%

Most occurring characters

ValueCountFrequency (%)
0 7152
93.1%
1 532
 
6.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 7684
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 7152
93.1%
1 532
 
6.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 7684
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 7152
93.1%
1 532
 
6.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 7684
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 7152
93.1%
1 532
 
6.9%
Distinct225
Distinct (%)2.9%
Missing0
Missing (%)0.0%
Memory size585.4 KiB
2025-06-07T16:01:56.540136image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length21
Median length21
Mean length21
Min length21

Characters and Unicode

Total characters161364
Distinct characters12
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique8 ?
Unique (%)0.1%

Sample

1st row2016-11-21/2016-11-27
2nd row2016-11-28/2016-12-04
3rd row2016-11-28/2016-12-04
4th row2016-11-28/2016-12-04
5th row2016-12-05/2016-12-11
ValueCountFrequency (%)
2018-11-05/2018-11-11 358
 
4.7%
2017-09-25/2017-10-01 321
 
4.2%
2016-12-12/2016-12-18 292
 
3.8%
2017-08-14/2017-08-20 259
 
3.4%
2017-09-11/2017-09-17 222
 
2.9%
2016-12-05/2016-12-11 188
 
2.4%
2018-08-13/2018-08-19 180
 
2.3%
2017-08-07/2017-08-13 179
 
2.3%
2018-10-29/2018-11-04 162
 
2.1%
2017-09-18/2017-09-24 143
 
1.9%
Other values (215) 5380
70.0%
2025-06-07T16:01:57.183441image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 35768
22.2%
- 30736
19.0%
1 28278
17.5%
2 26814
16.6%
7 7976
 
4.9%
/ 7684
 
4.8%
8 6344
 
3.9%
9 5665
 
3.5%
6 3197
 
2.0%
3 3115
 
1.9%
Other values (2) 5787
 
3.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 161364
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 35768
22.2%
- 30736
19.0%
1 28278
17.5%
2 26814
16.6%
7 7976
 
4.9%
/ 7684
 
4.8%
8 6344
 
3.9%
9 5665
 
3.5%
6 3197
 
2.0%
3 3115
 
1.9%
Other values (2) 5787
 
3.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 161364
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 35768
22.2%
- 30736
19.0%
1 28278
17.5%
2 26814
16.6%
7 7976
 
4.9%
/ 7684
 
4.8%
8 6344
 
3.9%
9 5665
 
3.5%
6 3197
 
2.0%
3 3115
 
1.9%
Other values (2) 5787
 
3.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 161364
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 35768
22.2%
- 30736
19.0%
1 28278
17.5%
2 26814
16.6%
7 7976
 
4.9%
/ 7684
 
4.8%
8 6344
 
3.9%
9 5665
 
3.5%
6 3197
 
2.0%
3 3115
 
1.9%
Other values (2) 5787
 
3.6%

Interactions

2025-06-07T16:01:31.275173image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-07T16:01:03.565700image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-07T16:01:06.998832image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-07T16:01:09.938430image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-07T16:01:13.755769image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-07T16:01:16.931708image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-07T16:01:19.615436image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-07T16:01:22.930483image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-07T16:01:26.307154image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-07T16:01:28.948284image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-07T16:01:31.520557image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-07T16:01:03.820518image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-07T16:01:07.298136image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-07T16:01:10.268113image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-07T16:01:14.032983image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-07T16:01:17.198686image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-07T16:01:19.889914image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-07T16:01:23.230191image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-07T16:01:26.590276image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-07T16:01:29.158793image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-07T16:01:32.058795image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-07T16:01:04.228644image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-07T16:01:07.584329image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-07T16:01:10.891146image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-07T16:01:14.326285image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-07T16:01:17.427662image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-07T16:01:20.174372image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-07T16:01:23.544848image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-07T16:01:26.955360image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-07T16:01:29.361908image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-07T16:01:32.298174image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-07T16:01:04.468006image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-07T16:01:07.903921image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-07T16:01:11.122028image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-07T16:01:14.627698image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-07T16:01:17.733258image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-07T16:01:20.669259image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-07T16:01:24.019181image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-07T16:01:27.210220image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-07T16:01:29.574954image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-07T16:01:32.596151image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-07T16:01:04.711798image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-07T16:01:08.286204image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-07T16:01:11.440529image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-07T16:01:14.950036image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-07T16:01:18.016614image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-07T16:01:20.950880image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-07T16:01:24.329802image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-07T16:01:27.455550image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-07T16:01:29.795824image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-07T16:01:32.916996image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-07T16:01:05.167688image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
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2025-06-07T16:01:11.788146image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-07T16:01:15.560995image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-07T16:01:18.415808image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
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2025-06-07T16:01:24.581045image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-07T16:01:27.727265image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-07T16:01:30.019092image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-07T16:01:33.325334image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-07T16:01:05.543395image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-07T16:01:08.943657image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-07T16:01:12.180385image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-07T16:01:15.865057image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-07T16:01:18.687658image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-07T16:01:21.580776image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-07T16:01:25.008907image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-07T16:01:28.057849image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-07T16:01:30.256673image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-07T16:01:33.575952image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-07T16:01:05.927961image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-07T16:01:09.255577image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-07T16:01:12.569738image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-07T16:01:16.155235image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-07T16:01:18.935508image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-07T16:01:21.927509image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-07T16:01:25.363919image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-07T16:01:28.329596image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-07T16:01:30.609962image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-07T16:01:33.840049image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-07T16:01:06.270128image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-07T16:01:09.502123image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-07T16:01:12.976812image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-07T16:01:16.407710image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-07T16:01:19.166004image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-07T16:01:22.212562image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-07T16:01:25.604611image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-07T16:01:28.523396image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-07T16:01:30.852913image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-07T16:01:34.172489image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-07T16:01:06.660153image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-07T16:01:09.736023image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-07T16:01:13.425282image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-07T16:01:16.685915image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-07T16:01:19.395751image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-07T16:01:22.511655image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-07T16:01:25.973868image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-07T16:01:28.739996image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-07T16:01:31.048922image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Correlations

2025-06-07T16:01:57.494452image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
customer_gendercustomer_idemail_domaingender_encodedhigh_value_itemitem_iditem_priceitem_qty_orderitem_unit_totalorder_day_of_weekorder_hourorder_idorder_totalproduct_idtotal_qty_ordered
customer_gender1.0000.8870.8300.9990.0000.1420.0450.0200.0000.1120.1180.1630.0000.1310.048
customer_id0.8871.0000.7040.8870.0800.1130.0800.0050.0810.111-0.0960.1150.0880.083-0.110
email_domain0.8300.7041.0000.8300.1070.3720.0190.1640.0000.1520.1030.2770.0850.1920.144
gender_encoded0.9990.8870.8301.0000.0000.1420.0450.0200.0000.1120.1180.1630.0000.1310.048
high_value_item0.0000.0800.1070.0001.0000.0750.5140.0350.2040.0000.0740.1000.1260.0850.039
item_id0.1420.1130.3720.1420.0751.0000.021-0.0120.0120.1040.0050.9990.0020.809-0.066
item_price0.0450.0800.0190.0450.5140.0211.000-0.1230.9870.024-0.0330.0210.6370.0640.049
item_qty_order0.0200.0050.1640.0200.035-0.012-0.1231.0000.0110.0080.018-0.012-0.0100.0070.211
item_unit_total0.0000.0810.0000.0000.2040.0120.9870.0111.0000.010-0.0290.0120.6440.0590.083
order_day_of_week0.1120.1110.1520.1120.0000.1040.0240.0080.0101.0000.1070.0910.0960.0820.057
order_hour0.118-0.0960.1030.1180.0740.005-0.0330.018-0.0290.1071.0000.005-0.0460.0010.067
order_id0.1630.1150.2770.1630.1000.9990.021-0.0120.0120.0910.0051.0000.0010.808-0.066
order_total0.0000.0880.0850.0000.1260.0020.637-0.0100.6440.096-0.0460.0011.0000.0020.388
product_id0.1310.0830.1920.1310.0850.8090.0640.0070.0590.0820.0010.8080.0021.000-0.091
total_qty_ordered0.048-0.1100.1440.0480.039-0.0660.0490.2110.0830.0570.067-0.0660.388-0.0911.000

Missing values

2025-06-07T16:01:34.912516image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
A simple visualization of nullity by column.
2025-06-07T16:01:35.830560image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

order_created_atitem_idorder_idorder_numberorder_totaltotal_qty_orderedcustomer_idcustomer_namecustomer_gendercustomer_emailproduct_idproduct_skuproduct_nameitem_priceitem_qty_orderitem_unit_totalorder_dateorder_monthorder_day_of_weekorder_hourhigh_value_itememail_domaingender_encodedorder_week
02016-11-23 12:12:16+00:00139A11230000104632705.00114Shantae GuseoMaledebest@verizon.net622312107687562107689422700.0012700.002016-11-232016-11Wednesday12Falseverizon.net12016-11-21/2016-11-27
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